{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.60      1.00      0.75         3\n",
      "          1       1.00      0.60      0.75         5\n",
      "\n",
      "avg / total       0.85      0.75      0.75         8\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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98emnn7JhwwYAunbtSmJiIs2aNQtoTJYUjDEmQHr16sX27dsZP348cXFxiJR2U2bNsqRg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\nWFIwxhjjYUnBGGOMhyUFY4wxHv8/xuKOy0dfDVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb7286d048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值为: 0.933333333333\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# 预测概率\n",
    "probabilities = np.array([0.74, 0.71, 0.52, 0.48, 0.45, 0.34, 0.21, 0.15])\n",
    "# 对应的标签\n",
    "labels = np.array([1, 1, 1, 1, 0, 1, 0, 0])\n",
    "\n",
    "# 按照预测概率从高到低排序\n",
    "indices = np.argsort(probabilities)[::-1]\n",
    "sorted_labels = labels[indices]\n",
    "\n",
    "# 计算真阳性率（TPR）和假阳性率（FPR）\n",
    "tpr = np.cumsum(sorted_labels) / np.sum(sorted_labels)\n",
    "fpr = np.cumsum(1 - sorted_labels) / np.sum(1 - sorted_labels)\n",
    "\n",
    "# 将概率转化为预测标签\n",
    "predicted_labels = (probabilities > 0.5).astype(int)\n",
    "\n",
    "# 打印分类报告\n",
    "print(\"分类报告:\\n\", classification_report(labels, predicted_labels))\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label='ROC curve')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--', lw=2, label='Random guess')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 计算AUC值\n",
    "auc = np.trapz(tpr, fpr)\n",
    "print(\"AUC值为:\", auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "cell_id": "c22004622a0b45309309a5013143f866",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 67
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1963,
    "execution_start": 1657008283060,
    "id": "9CAADB208C054BC0884C766E699777B8",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "source_hash": "6c9d642f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集大小: 1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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bMZnGzdfBq7Awr7sUCAKlIDR/IO7CBFAHLPb06O+v0mAbgk4FIX9UtgFem9Zy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\nQAkICAgI8IL/D/Z3S908W01fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a19f93908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "# 从源文件中读入数据并处理\n",
    "lines = np.loadtxt('lr_dataset.csv', delimiter=',', dtype=float)\n",
    "x_total = lines[:, 0:2]\n",
    "y_total = lines[:, 2]\n",
    "print('数据集大小:', len(x_total))\n",
    "\n",
    "# 将得到的数据在二维平面上制图，不同的类别染上不同的颜色以便于观察样本点的分布\n",
    "pos_index = np.where(y_total == 1)\n",
    "neg_index = np.where(y_total == 0)\n",
    "plt.scatter(x_total[pos_index, 0], x_total[pos_index, 1], marker='o', color='coral', s=10)\n",
    "plt.scatter(x_total[neg_index, 0], x_total[neg_index, 1], marker='x', color='blue', s=10)\n",
    "plt.xlabel('X1 axis')\n",
    "plt.ylabel('X2 axis')\n",
    "plt.show()\n",
    "\n",
    "# 划分训练集与测试集\n",
    "np.random.seed(0)\n",
    "ratio = 0.7\n",
    "split = int(len(x_total) * ratio)\n",
    "idx = np.random.permutation(len(x_total))\n",
    "x_total = x_total[idx]\n",
    "y_total = y_total[idx]\n",
    "x_train, y_train = x_total[:split], y_total[:split]\n",
    "x_test, y_test = x_total[split:], y_total[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cell_id": "18f0f72ab4944e6e81d1482fe65f498d",
    "collapsed": true,
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 79
    },
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "source": [
    "def acc(y_true, y_pred):\n",
    "    return np.mean(y_true == y_pred)\n",
    "\n",
    "def auc(y_true, y_pred):\n",
    "    # 按预测值从大到小排序，越靠前的样本预测正类概率越大\n",
    "    idx = np.argsort(y_pred)[::-1]\n",
    "    y_true = y_true[idx]\n",
    "    y_pred = y_pred[idx]\n",
    "    # 把y_pred中不重复的值当作阈值，依次计算FP样本和TP样本数量\n",
    "    # 由于两个数组已经排序且位置对应，直接从前向后累加即可\n",
    "    tp = np.cumsum(y_true) \n",
    "    fp = np.cumsum(1 - y_true)\n",
    "    tpr = tp / tp[-1]\n",
    "    fpr = fp / fp[-1]\n",
    "    # 依次枚举FPR，计算曲线下的面积\n",
    "    # 方便起见，给FPR和TPR最开始添加(0,0)\n",
    "    s = 0.0\n",
    "    tpr = np.concatenate([[0], tpr])\n",
    "    fpr = np.concatenate([[0], fpr])\n",
    "    for i in range(1, len(fpr)):\n",
    "        s += (fpr[i] - fpr[i - 1]) * tpr[i]\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cell_id": "35e2fa006b994a3696f9c6f0906ded24",
    "collapsed": true,
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 91
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 440,
    "execution_start": 1657008285031,
    "id": "4016220C21214BB0A10AD46D7CCC643F",
    "jupyter": {},
    "notebookId": "60364fc9c614ab001504d12c",
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "source_hash": "c24ea556",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 逻辑斯谛函数\n",
    "def logistic(z):\n",
    "    return 1 / (1 + np.exp(-z))\n",
    "\n",
    "def GD(num_steps, learning_rate, l2_coef):\n",
    "    # 初始化模型参数\n",
    "    theta = np.random.normal(size=(X.shape[1],))\n",
    "    train_losses = []\n",
    "    test_losses = []\n",
    "    train_acc = []\n",
    "    test_acc = []\n",
    "    train_auc = []\n",
    "    test_auc = []\n",
    "    for i in range(num_steps):\n",
    "        pred = logistic(X @ theta)\n",
    "        grad = -X.T @ (y_train - pred) + l2_coef * theta\n",
    "        theta -= learning_rate * grad\n",
    "        # 记录损失函数\n",
    "        train_loss = - y_train.T @ np.log(pred) \\\n",
    "                     - (1 - y_train).T @ np.log(1 - pred) \\\n",
    "                     + l2_coef * np.linalg.norm(theta) ** 2 / 2\n",
    "        train_losses.append(train_loss / len(X))\n",
    "        test_pred = logistic(X_test @ theta)\n",
    "        test_loss = - y_test.T @ np.log(test_pred) \\\n",
    "                    - (1 - y_test).T @ np.log(1 - test_pred)\n",
    "        test_losses.append(test_loss / len(X_test))\n",
    "        # 记录各个评价指标，阈值采用0.5\n",
    "        train_acc.append(acc(y_train, pred >= 0.5))\n",
    "        test_acc.append(acc(y_test, test_pred >= 0.5))\n",
    "        train_auc.append(auc(y_train, pred))\n",
    "        test_auc.append(auc(y_test, test_pred))\n",
    "        \n",
    "    return theta, train_losses, test_losses, train_acc, test_acc, train_auc, test_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "cell_id": "f9fe14d0a6fa4782902b6b1764372eac",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 103
    },
    "deepnote_cell_type": "code",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测准确率： 0.876666666667\n",
      "回归系数： [ 3.13501827  2.90998799  0.55095127]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZkEqt9bnA0+7eAxgFPGdmOYSJZL8JnBetR5vZt6t8QB3VdieycWNYd+iQoHDb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CKtjLg5iT/zTOA+p1w5ksOoM0dxtjRm8Ac38tryNzlj9Xpvg+2Q6EF97JYqM51wykI0m/twCks6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a1b5d6f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义梯度下降迭代的次数，学习率，以及L2正则系数\n",
    "num_steps = 250\n",
    "learning_rate = 0.002\n",
    "l2_coef = 1.0\n",
    "np.random.seed(0)\n",
    "\n",
    "# 在x矩阵上拼接1\n",
    "X = np.concatenate([x_train, np.ones((x_train.shape[0], 1))], axis=1)\n",
    "X_test = np.concatenate([x_test, np.ones((x_test.shape[0], 1))], axis=1) \n",
    "\n",
    "theta, train_losses, test_losses, train_acc, test_acc, train_auc, test_auc = GD(num_steps, learning_rate, l2_coef)\n",
    "\n",
    "# 计算测试集上的预测准确率\n",
    "y_pred = np.where(logistic(X_test @ theta) >= 0.5, 1, 0)\n",
    "final_acc = acc(y_test, y_pred)\n",
    "print('预测准确率：', final_acc)\n",
    "print('回归系数：', theta)\n",
    "\n",
    "plt.figure(figsize=(13, 9))\n",
    "xticks = np.arange(num_steps) + 1\n",
    "# 绘制训练曲线\n",
    "plt.subplot(221)\n",
    "plt.plot(xticks, train_losses, color='blue', label='train loss')\n",
    "plt.plot(xticks, test_losses, color='red', ls='--', label='test loss')\n",
    "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制准确率\n",
    "plt.subplot(222)\n",
    "plt.plot(xticks, train_acc, color='blue', label='train accuracy')\n",
    "plt.plot(xticks, test_acc, color='red', ls='--', label='test accuracy')\n",
    "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制AUC\n",
    "plt.subplot(223)\n",
    "plt.plot(xticks, train_auc, color='blue', label='train AUC')\n",
    "plt.plot(xticks, test_auc, color='red', ls='--', label='test AUC')\n",
    "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('AUC')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制模型学到的分隔直线\n",
    "plt.subplot(224)\n",
    "plot_x = np.linspace(-1.1, 1.1, 100)\n",
    "# 直线方程：theta_0 * x_1 + theta_1 * x_2 + theta_2 = 0\n",
    "plot_y = -(theta[0] * plot_x + theta[2]) / theta[1]\n",
    "pos_index = np.where(y_total == 1)\n",
    "neg_index = np.where(y_total == 0)\n",
    "plt.scatter(x_total[pos_index, 0], x_total[pos_index, 1], marker='o', color='coral', s=10)\n",
    "plt.scatter(x_total[neg_index, 0], x_total[neg_index, 1], marker='x', color='blue', s=10)\n",
    "plt.plot(plot_x, plot_y, ls='-.', color='green')\n",
    "plt.xlim(-1.1, 1.1)\n",
    "plt.ylim(-1.1, 1.1)\n",
    "plt.xlabel('X1 axis')\n",
    "plt.ylabel('X2 axis')\n",
    "plt.savefig('output_16_1.png')\n",
    "plt.savefig('output_16_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "cell_id": "67ad3ccca5fa4802afb9d37a5faec618",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 127
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1179,
    "execution_start": 1657008285478,
    "id": "767C5FC7004C43368A0737F9BEBEEBEE",
    "jupyter": {},
    "notebookId": "60364fc9c614ab001504d12c",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "source_hash": "1fa06f0e",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回归系数： [ 3.14129907  2.91620111] [ 0.5518978]\n",
      "准确率为： 0.876666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# 使用线性模型中的逻辑回归模型在数据集上训练\n",
    "# 其提供的liblinear优化算法适合在较小数据集上使用\n",
    "# 默认使用系数为1.0的L2正则化约束\n",
    "# 其他可选参数请参考官方文档\n",
    "lr_clf = LogisticRegression(solver='liblinear')\n",
    "lr_clf.fit(x_train, y_train)\n",
    "print('回归系数：', lr_clf.coef_[0], lr_clf.intercept_)\n",
    "\n",
    "# 在数据集上用计算得到的逻辑回归模型进行预测并计算准确度\n",
    "y_pred = lr_clf.predict(x_test)\n",
    "print('准确率为：',np.mean(y_pred == y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "deepnote": {},
  "deepnote_app_layout": "article",
  "deepnote_execution_queue": [],
  "deepnote_notebook_id": "994aeaccff4a4432a2cab1b55fa42b9b",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "d12a6980af1de3549060b7b451d48d445ec6b4aaeaf0b0e12a509d2182e95745"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
